The goal of this clinical trial is to learn whether an AI-powered carbohydrate counting educational platform can help parents of children with type 1 diabetes improve their carbohydrate counting skills and diabetes management. The study will include parents or primary caregivers of children aged 2-12 years with type 1 diabetes. The main questions it aims to answer are: * Is the AI-powered educational platform feasible, acceptable, and easy for parents to use? * Can the platform improve carbohydrate counting accuracy, parental confidence in diabetes management, and diabetes outcomes compared with usual education alone? Researchers will compare parents who receive access to the AI-powered carbohydrate counting educational platform plus usual diabetes education with parents who receive usual diabetes education alone to see whether the AI-supported approach provides additional benefits. Participants will: * Complete baseline assessments, including questionnaires and a carbohydrate counting test. * Be randomly assigned to either the AI-supported education group or the usual education group. * Use the assigned educational resources for 12 weeks. * Complete a follow-up assessment at 6 weeks and a final assessment at 12 weeks. * Provide information about their child's diabetes management, including HbA1c and glucose monitoring data. * Complete questionnaires about confidence, usability, and satisfaction with the educational support they receive. The AI platform is designed to provide educational support only and does not replace medical advice, insulin dosing decisions, or routine diabetes care provided by healthcare professionals.
This multicentre randomized controlled feasibility trial will evaluate an AI-powered educational platform designed to support carbohydrate counting education for parents of children with type 1 diabetes (T1D). Accurate carbohydrate counting is an essential component of T1D management because insulin dosing is closely linked to carbohydrate intake. However, many parents experience challenges in estimating carbohydrate content accurately, which may affect glycemic control. The intervention uses conversational artificial intelligence to provide personalized educational support, interactive learning opportunities, and practical guidance related to carbohydrate counting. The platform is intended as an educational tool and does not provide medical advice or insulin dosing recommendations. Educational content and safety oversight are provided by pediatric endocrinologists, diabetes educators, and registered dietitians. The primary objective of this feasibility study is to evaluate recruitment, retention, participant engagement, intervention adherence, and data collection procedures to determine whether a future definitive efficacy trial is warranted. Secondary objectives include assessment of participant acceptability and usability, as well as exploration of preliminary effects on carbohydrate counting accuracy, parental diabetes management self-efficacy, and glycemic outcomes. Participants will be recruited from our pediatric diabetes centers, and randomized to receive either access to the AI-powered educational platform in addition to enhanced usual care or enhanced usual care alone. Study findings will inform the development of larger trials evaluating the role of conversational artificial intelligence in diabetes education and chronic disease self-management.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
SINGLE
Enrollment
80
Participants will receive access to an AI-powered carbohydrate counting educational platform designed for parents of children with type 1 diabetes. The platform provides interactive carbohydrate counting education, personalized educational guidance, carbohydrate estimation practice, natural-language question answering, and scenario-based learning. Participants will use the platform for 12 weeks in addition to enhanced usual diabetes care. All educational content is developed and supervised by pediatric endocrinologists, certified diabetes educators, and registered dietitians. The platform functions as an educational support tool only and does not provide medical advice or insulin dosing recommendations.
Sultan Qaboos University Hospital
Muscat, Oman
Al Jalila Children's Hospital
Dubai, United Arab Emirates
Al Qasimi Hospital
Sharjah city, United Arab Emirates
Recruitment Rate
Number and proportion of eligible participants recruited into the study across participating sites.
Time frame: 12 months
Retention Rate
Proportion of enrolled participants who complete the 12-week follow-up assessment.
Time frame: 12 weeks
Intervention Adherence
Proportion of participants in the intervention group who engage with the AI-powered carbohydrate counting educational platform for at least 10 sessions during the study period.
Time frame: 12 weeks
Data Completeness
Proportion of participants with complete primary outcome data collected at baseline and 12-week follow-up assessments.
Time frame: 12 weeks
Acceptability of Intervention Measure
Participant-rated acceptability of the AI-powered carbohydrate counting educational platform using the validated 4-item Acceptability of Intervention Measure.
Time frame: 12 weeks
System Usability Scale
Participant-rated usability of the AI-powered carbohydrate counting educational platform measured using the System Usability Scale (SUS), a validated 10-item questionnaire. The SUS total score ranges from 0 to 100, with higher scores indicating better perceived usability.
Time frame: 12 weeks
Net Promoter Score
Participant likelihood of recommending the AI-powered carbohydrate counting educational platform to other parents of children with type 1 diabetes, measured using the Net Promoter Score (NPS). Participants rate their likelihood of recommending the platform on a scale from 0 (Not at all likely) to 100 (Extremely likely). Higher scores indicate a greater likelihood of recommending the intervention.
Time frame: 12 weeks
Carbohydrate Counting Accuracy
Change in carbohydrate counting accuracy assessed using a standardized carbohydrate counting assessment. Accuracy will be defined as the percentage of estimates within 20% of dietitian-calculated carbohydrate values.
Time frame: Baseline and 12 weeks
Time in Range (70-180 mg/dL)
Percentage of time glucose values remain within the target range of 70-180 mg/dL based on continuous glucose monitoring or glucose meter data.
Time frame: Baseline and 12 weeks
Glucose Variability
Change in glucose variability measured by coefficient of variation of glucose values.
Time frame: Baseline and 12 weeks
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